# Load necessary packages
pacman::p_load(tidyverse,
knitr,
here,
dplyr,
janitor,
plotly)
# Import the communicable diseases data
data_cmnn <- read_csv(here("data", "burden-of-disease-cmnn.csv"))
## Rows: 8100 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Entity, Code
## dbl (2): Year, DALYs (Disability-Adjusted Life Years) - Communicable, matern...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Here render a table for the DALY burden over time for the three countries
#clean row names and then filter the countries
data_cmnn_orig_eafrica <-
data_cmnn %>%
clean_names() %>%
filter(entity %in% c("Uganda","Kenya","Tanzania")) %>%
dplyr::rename(cmnn_srate =
dal_ys_disability_adjusted_life_years_communicable_maternal_neonatal_and_nutritional_diseases_sex_both_age_age_standardized_rate,
country= entity)
# You should pivot the data to show each country in a separate column.
data_cmnn_orig_eafrica_wide <-
data_cmnn_orig_eafrica %>%
select(!code) %>%
pivot_wider(values_from = cmnn_srate,
names_from = country
)
data_cmnn_orig_eafrica_wide
## # A tibble: 30 × 4
## year Kenya Tanzania Uganda
## <dbl> <dbl> <dbl> <dbl>
## 1 1990 34322. 51624. 75945.
## 2 1991 36630. 53380. 76978.
## 3 1992 39510. 55460. 77512.
## 4 1993 42581. 56854. 77695.
## 5 1994 45822. 58453. 76949.
## 6 1995 48346. 59609. 76070.
## 7 1996 50629. 60213. 74909.
## 8 1997 52614. 60717. 73727.
## 9 1998 53894. 60494. 72244.
## 10 1999 54448. 59438. 70524.
## # ℹ 20 more rows
# Use kable() from the knitr package to render the table.
tab_rendered <-
knitr::kable(data_cmnn_orig_eafrica_wide)
tab_rendered
| year | Kenya | Tanzania | Uganda |
|---|---|---|---|
| 1990 | 34321.93 | 51624.16 | 75944.97 |
| 1991 | 36629.81 | 53379.70 | 76978.02 |
| 1992 | 39510.43 | 55460.50 | 77511.61 |
| 1993 | 42581.44 | 56854.18 | 77695.11 |
| 1994 | 45822.47 | 58452.76 | 76948.73 |
| 1995 | 48345.59 | 59609.03 | 76070.29 |
| 1996 | 50629.20 | 60212.76 | 74909.36 |
| 1997 | 52614.39 | 60717.00 | 73726.62 |
| 1998 | 53893.87 | 60493.72 | 72243.52 |
| 1999 | 54448.17 | 59437.74 | 70524.46 |
| 2000 | 54031.62 | 58112.20 | 68550.38 |
| 2001 | 53562.06 | 56280.20 | 65942.75 |
| 2002 | 52740.17 | 54165.05 | 63032.73 |
| 2003 | 51644.87 | 51870.74 | 60345.84 |
| 2004 | 49622.21 | 49818.96 | 57778.01 |
| 2005 | 46850.49 | 47862.23 | 53612.07 |
| 2006 | 43667.25 | 45149.29 | 49145.64 |
| 2007 | 40418.84 | 41560.02 | 46258.95 |
| 2008 | 37667.66 | 38556.13 | 43621.19 |
| 2009 | 35177.11 | 36383.30 | 41071.17 |
| 2010 | 32880.90 | 34485.90 | 38831.56 |
| 2011 | 30877.60 | 32753.08 | 36196.78 |
| 2012 | 29278.72 | 30265.72 | 33312.56 |
| 2013 | 28096.14 | 27687.76 | 30292.27 |
| 2014 | 27007.80 | 26129.75 | 28099.68 |
| 2015 | 25610.50 | 24751.08 | 26900.48 |
| 2016 | 24209.18 | 23766.85 | 26645.55 |
| 2017 | 22982.12 | 22638.19 | 24668.12 |
| 2018 | 22034.76 | 21187.60 | 22896.94 |
| 2019 | 21312.06 | 20117.73 | 21904.64 |
# Here, plot a line graph showing the trend of DALY burden for the chosen countries over time. You can use ggplot2 to create this plot. Each country's line should be a different color.
ggplot_cmnn <-
ggplot(data =data_cmnn_orig_eafrica,
mapping = aes(x= year,
y = cmnn_srate ,
color = country))+
geom_line()
ggplotly(ggplot_cmnn)
Provide a brief analysis based on the data presented in the table and chart. Highlight any significant findings or patterns. About 3 sentences.
DALYs for a disease or health condition are the sum of the years of life lost to due to premature mortality (YLLs) and the years lived with a disability (YLDs) due to prevalent cases of the disease or health condition in a population
According to the plot above, Uganda had slightly more years lost due to premature mortality due to the Communicable, Maternal, Neonatal, and Nutritional Disease Burden (CMNN) compared to its Kenyan and Tanzania counterparts in the East African Community
Additionaly life years lost have decreased over the years from 1990 to 2020
The data also shows that there was a notable disparity in the sum of years lost due to CMNN upto about 2005 among the three countries but with all countries registering steady decreases in the same and there being not notable variations for the next 15 years